Predictive maintenance utilizing supervised sequence rule mining

ABSTRACT

Statistically significant event patterns predict the timing for performing entity maintenance. Event patterns are determined based on a target variable having an undesired value for a given entity when the event pattern occurs. Event patterns are filtered based on distributions of the event patterns across multiple entities and distributions of event patterns during desired operation of the entities and undesired operation of the entities. A predictive maintenance process is established having significant event patterns as the basis for maintenance tasks.

BACKGROUND

The present invention relates generally to the field of data processing,and more particularly to predictive maintenance utilizing data mining.

Data mining is the computing process of discovering patterns in largedata sets. The overall goal of the data mining process is to extractinformation from a data set and transform it into an understandablestructure for further use. Sequential pattern mining is a topic of datamining concerned with finding statistically relevant patterns betweendata examples where the values are delivered in a sequence. Applicationsof sequential pattern mining include medical treatment assistance,natural disaster prediction, stocks and financial market analysis, DNAsequencing, and gene structuralizing.

Predictive maintenance techniques are designed to help determine thecondition of in-service equipment in order to predict when maintenanceshould be performed. This approach promises cost savings over routine ortime-based preventive maintenance, because tasks are performed only whenwarranted. Preventive maintenance is characterized by periodicmaintenance operations in order to avoid equipment failures or machinerybreakdowns, determined through optimal preventive maintenance schedulingusing a wide range of models describing the degrading process ofequipment, cost structure, and admissible maintenance actions.

SUMMARY

In one aspect of the present invention, a computer-implemented method, acomputer program product, and a system for establishing a predictivemaintenance process includes: determining a target variable for aprocess to be performed by a plurality of entities; collecting eventdata from the plurality of entities while performing the process, theevent data including data time stamps; storing, in a database, the eventdata; recording to the database a set of values of the target variableduring the process, the values including value time stamps; assigning toeach entity a set of entity data from the collected event data, each setof entity data corresponding to the entity from which the event data wascollected; identifying desired periods of time when the set of valuesmeet a desired value range of the target variable and undesired periodsof time when the set of values do not meet the desired value range basedon the value time stamps; associating the desired periods of time withcollected event data having corresponding data time stamps, theassociated event data being produced during periods of time when thevalue of the target variable is desired; storing the associated eventdata produced during periods of time when the value of the targetvariable is desired in the database as desired result data; associatingthe undesired periods of time with collected event data havingcorresponding data time stamps, the associated event data being producedduring periods of time when the value of the target variable isundesired; storing the associated event data produced during periods oftime when the value of the target variable is undesired in the databaseas undesired result data; mining for a set of sequence rules within theundesired result data for each entity, the sequence rules being based ona chronological order of data time stamps; identifying a subset ofsequence rules having a uniform occurrence among the plurality ofentities; selecting a set of candidate sequence rules from the subset ofsequence rules based on a non-uniform distribution of the set ofcandidate sequence rules within the desired result data for each entity;and recording a set of significant sequence rules from the set ofcandidate sequence rules as a set of predictive maintenance rules for apredictive maintenance process based on a disparate distribution of theset of significant sequence rules when comparing distributions of theoccurrence of candidate sequence rules in desired result data andundesired result data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram view of a first embodiment of a systemaccording to the present invention;

FIG. 2 is a flowchart showing a first embodiment method performed, atleast in part, by the first embodiment system;

FIG. 3 is a block diagram view of a machine logic (e.g., software)portion of the first embodiment system; and

FIG. 4 is a screenshot generated by the first embodiment system.

DETAILED DESCRIPTION

Statistically significant event patterns predict the timing forperforming entity maintenance. Event patterns are determined based on atarget variable having an undesired value for a given entity when theevent pattern occurs. Event patterns are filtered based on distributionsof the event patterns across multiple entities and distributions ofevent patterns during desired operation of the entities and undesiredoperation of the entities. A predictive maintenance process isestablished having significant event patterns as the basis formaintenance tasks. This Detailed Description section is divided into thefollowing sub-sections: (i) Hardware and Software Environment; (ii)Example Embodiment; (iii) Further Comments and/or Embodiments; and (iv)Definitions.

I. Hardware and Software Environment

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

An embodiment of a possible hardware and software environment forsoftware and/or methods according to the present invention will now bedescribed in detail with reference to the Figures. FIG. 1 is afunctional block diagram illustrating various portions of networkedcomputers system 100, including: predictive maintenance serversub-system 102; machine 104, sensor 106, computer 108, sensor 110,computer 112; and communication network 114. Predictive maintenanceserver sub-system 102 contains: predictive maintenance server computer200; display device 212; and external devices 214. Predictivemaintenance server computer 200 contains: communication unit 202;processor set 204; input/output (I/O) interface set 206; memory device208; and persistent storage device 210. Memory device 208 contains:random access memory (RAM) devices 216; and cache memory device 218.Persistent storage device 210 contains: predictive maintenance program300 and database 220.

Predictive maintenance server sub-system 102 is, in many respects,representative of the various computer sub-systems in the presentinvention. Accordingly, several portions of predictive maintenanceserver sub-system 102 will now be discussed in the following paragraphs.

Predictive maintenance server sub-system 102 may be a laptop computer, atablet computer, a netbook computer, a personal computer (PC), a desktopcomputer, a personal digital assistant (PDA), a smart phone, or anyprogrammable electronic device capable of communicating with clientsub-systems via communication network 114. Predictive maintenanceprogram 300 is a collection of machine readable instructions and/or datathat is used to create, manage, and control certain software functionsthat will be discussed in detail, below, in the Example Embodimentsub-section of this Detailed Description section.

Predictive maintenance server sub-system 102 is capable of communicatingwith other computer sub-systems via communication network 114.Communication network 114 can be, for example, a local area network(LAN), a wide area network (WAN) such as the Internet, or a combinationof the two, and can include wired, wireless, or fiber optic connections.In general, communication network 114 can be any combination ofconnections and protocols that will support communications betweenserver and client sub-systems.

Predictive maintenance server sub-system 102 is shown as a block diagramwith many double arrows. These double arrows (no separate referencenumerals) represent a communications fabric, which providescommunications between various components of predictive maintenanceserver sub-system 102. This communications fabric can be implementedwith any architecture designed for passing data and/or controlinformation between processors (such as microprocessors, communicationsprocessors, and/or network processors, etc.), system memory, peripheraldevices, and any other hardware components within a system. For example,the communications fabric can be implemented, at least in part, with oneor more buses.

Memory device 208 and persistent storage device 210 are computerreadable storage media. In general, memory device 208 can include anysuitable volatile or non-volatile computer readable storage media. It isfurther noted that, now and/or in the near future: (i) external devices214 may be able to supply some, or all, memory for predictivemaintenance server sub-system 102; and/or (ii) devices external topredictive maintenance server sub-system 102 may be able to providememory for predictive maintenance server sub-system 102.

Predictive maintenance program 300 is stored in persistent storagedevice 210 for access and/or execution by one or more processors ofprocessor set 204, usually through memory device 208. Persistent storagedevice 210: (i) is at least more persistent than a signal in transit;(ii) stores the program (including its soft logic and/or data) on atangible medium (such as magnetic or optical domains); and (iii) issubstantially less persistent than permanent storage. Alternatively,data storage may be more persistent and/or permanent than the type ofstorage provided by persistent storage device 210.

Predictive maintenance program 300 may include both substantive data(that is, the type of data stored in a database) and/or machine readableand performable instructions. In this particular embodiment (i.e., FIG.1), persistent storage device 210 includes a magnetic hard disk drive.To name some possible variations, persistent storage device 210 mayinclude a solid-state hard drive, a semiconductor storage device, aread-only memory (ROM), an erasable programmable read-only memory(EPROM), a flash memory, or any other computer readable storage mediathat is capable of storing program instructions or digital information.

The media used by persistent storage device 210 may also be removable.For example, a removable hard drive may be used for persistent storagedevice 210. Other examples include optical and magnetic disks, thumbdrives, and smart cards that are inserted into a drive for transfer ontoanother computer readable storage medium that is also part of persistentstorage device 210.

Communication unit 202, in these examples, provides for communicationswith other data processing systems or devices external to predictivemaintenance server sub-system 102. In these examples, communication unit202 includes one or more network interface cards. Communication unit 202may provide communications through the use of either or both physicaland wireless communications links. Any software modules discussed hereinmay be downloaded to a persistent storage device (such as persistentstorage device 210) through a communications unit (such as communicationunit 202).

I/O interface set 206 allows for input and output of data with otherdevices that may be connected locally in data communication withpredictive maintenance server computer 200. For example, I/O interfaceset 206 provides a connection to external devices 214. External devices214 will typically include devices, such as a keyboard, a keypad, atouch screen, and/or some other suitable input device. External devices214 can also include portable computer readable storage media, such as,for example, thumb drives, portable optical or magnetic disks, andmemory cards. Software and data used to practice embodiments of thepresent invention (e.g., predictive maintenance program 300) can bestored on such portable computer readable storage media. In theseembodiments, the relevant software may (or may not) be loaded, in wholeor in part, onto persistent storage device 210 via I/O interface set206. I/O interface set 206 also connects in data communication withdisplay device 212.

Display device 212 provides a mechanism to display data to a user andmay be, for example, a computer monitor or a smart phone display screen.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus, theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

II. Example Embodiment

FIG. 2 shows flowchart 250 depicting a method according to the presentinvention. FIG. 3 shows predictive maintenance program 300, whichperforms at least some of the method operations of flowchart 250. Thismethod and associated software will now be discussed, over the course ofthe following paragraphs, with extensive reference to FIG. 2 (for themethod operation blocks) and FIG. 3 (for the software blocks).

Processing begins at operation S252, where data collection module(“mod”) 352 collects data from entities and stores the data in database220. Data stored in the database includes timestamps for when the datais collected and is assigned in the next step a “data status.” Entitiesare any device that produces measured data, such as machine 104 (seeFIG. 1). Data may be collected from entities by data collection devicessuch as sensor 106, computer 108, sensor 110, and computer 112. The datamay also be collected by user input. The data collected for predictivemaintenance program 300 is, in some embodiments of the presentinvention, from systems monitoring functions and conditions of entitieswhere a vast amount of data is generated such that it is difficult tomaintain the data manually. In this embodiment, several entities at achemical plant are monitored during production of a chemical compound.Sensors monitor pipe flow, temperature, and pressure, while computersmonitor adsorption rate, distillation rate, evaporation rate, andsublimation rate. These sensors and computers are automated to collectentity data at regular intervals and to send the entity data topredictive maintenance program 300 upon collection.

Processing proceeds to operation S254, where data status mod 354determines a data status where the status is either desired or undesiredaccording to whether the entity associated with the data is operatingdesirably with respect to a value of a target variable. During a timewhen the value of the target variable is desired, data collectedcorresponds to desired-status data. When the value is not desired, datacollected corresponds to undesired status data. The determined datastatus is assigned to the data in the database according to thedetermination. Each entity produces at various times desired andundesired values for the target variable depending on individualoperation of the entity. Data is divided according to desired andundesired statuses based on pre-defined target variables thatdistinguish the data by status of an entity. For example, a particulartarget variable is introduced to correspond to a particular eventstatus, whether desired or undesired. Data assigned a desired status isindicative of a desired event status associated with the entity fromwhich the data is collected. Data assigned an undesired status isindicative of an undesired event status associated with the entity fromwhich the data is collected. Target variables are generated fromanalysis of conditions existing during desired and undesired events withrespect to the target variable. For example, the ideal temperature rangeof a chemical in a tank at the chemical plant is determined to be from50 degrees Celsius to 120 degrees Celsius. In this example, there arefive tanks in the chemical plant. The target variable is the temperatureof each tank. A desired status is assigned to collected data when tanktemperatures range between 50° C. and 120° C. (the target variable beingtemperature of the chemical). An undesired status is assigned tocollected data when tank temperatures are outside that range. The datacollected for the tanks is divided according to the statuses of thetanks with respect to the target variable when the data is collected.

Processing proceeds to operation S256, where candidate ruledetermination mod 356 determines candidate rules using data assigned anundesired status. A candidate rule is a statistically significantsequence rule that is generated from data having an undesired status.Sequence rules are identified within the undesired status data byapplying pattern analysis to perform supervised sequence rule mining.Sequential rule mining is similar to sequential pattern mining except ittakes into consideration the probability that a pattern will occur.Supervised sequence rule mining associated sequence rules with a targetvariable, such as the status being undesired data.

A sequential rule is a rule of the form X→Y where X and Y are sets ofitems. The rule X→Y is interpreted to mean that if items in set X occurin any order, then the items in set Y will follow in any order. To findsequential rules, two aspects are generally factored in: the support andthe confidence. The support of a rule X→Y identifies how many sequencescontain the items from set X followed by the items from set Y. Theconfidence of a rule X→Y is the support of the rule divided by thenumber of sequences containing the items from set X. It can beunderstood as the conditional probability P(Y|X) and it may be expressedas a percentage or decimal.

Candidate rules may be determined based on external factors that areinherent in the processes of an entity. A candidate rule thatsuccessfully predicts production of undesired status data is called aninfluential rule. An influential rule is a candidate rule that isdetermined to be a predictor of an error event and is used to establisha predictive maintenance process that monitors a process for theinfluential rule. Candidate rules may also be determined by supervisedsequence rule mining of event sequences in multiple entities.

FIG. 4 is screenshot 400 showing a sample chart of candidate rules andtheir corresponding support values. These candidate rules are thoserules determined to be potentially influential rules to be applied in apredictive maintenance process. Items A, B, and C are unique eventsamong multiple entities. The 1-level-rule type is a rule with one eventtaking place, A, B, or C. The 2-level-rule type is a rule with twoevents occurring in a specified order. For example, the rule B→A meansevent B is followed by event A. The 3-level-rule type is a rule withthree events taking place in the rule in a specified order. For example,the rule B→A→C means event B is followed by event A, which is thenfollowed by event C. The confidence value is the conditional probabilityof the occurrence of the corresponding rule. For example, the rule B→Ahas a confidence value of 0.5, so every time event B occurs, there is a50% chance that A occurs subsequently. According to some embodiments ofthe present invention, candidate rules having higher than averageconfidence values may be selected as candidate rules by predictivemaintenance program 300. Alternatively, predictive maintenance program300 specifies that sequence rules must have a minimum number of events,or be a specified rule type(s), to be selected as candidate rules. Inthis embodiment, candidate rules are selected based on analysis of eventdata generated while a chemical is transported to tanks. The candidaterules are selected by sequential pattern mining of the undesired statusdata produced as the chemical is processed at the plant. In thisexample, rules must include at least three events (3-level-rule min) tobe selected as candidate rules. Further, in this example, predictivemaintenance program 300 selects 20 rules with the highest relativeconfidence values as candidate rules.

Processing proceeds to operation S258, where uniform distributiondetermination mod 358 determines if candidate rules are uniformlydistributed within undesired status data. A uniform distribution of acandidate rule indicates a likelihood that the candidate rule isinfluential and has a general applicability to the process. Predictivemaintenance program 300 evaluates each candidate rule to determine if acandidate rule occurs on a specified percentage of entities whenproducing undesired status data. In some embodiments of the presentinvention, the specified percentage is established by a user.Alternatively, the specified percentage is established by general policyor by computer algorithm to automatically determine the specifiedpercentage. The selected candidate rules are evaluated for having asignificant presence in the undesired status data. In some embodiments,statistical methods are employed to determine a degree of significanceof the presence. For example, a one-sample chi-squared test may beemployed to determine if an occurrence of a candidate rule is equallydistributed throughout each entity. A chi-squared test uses counts orfrequencies of occurrence rather than means and standard deviations ofthe occurrences. It compares how many items actually fall into onecategory instead of another with respect to a hypothesized or expectedcount.

The Chi-squared test used with one sample is a method to measure fit ofdistributions. It is used to determine whether a distribution offrequencies for a variable in a sample is representative of a specifiedpopulation distribution. Measures of fit summarize the discrepancybetween observed values and the values expected under the model inquestion. The target frequency of occurrence of a candidate rule withincollected data from an entity is specified to perform the chi-squaredtest. The resulting value can be compared to the chi-squareddistribution to determine the degree of fit. In order to determine thedegrees of freedom of the chi-squared distribution, the number ofestimated parameters is subtracted from the total number of observedfrequencies. According to some embodiments of the present invention, theminimum degree of fit is specified for use by predictive maintenanceprogram 300. In this embodiment, predictive maintenance program 300requires that candidate rules must occur on 75% of the entitiesproducing undesired status data.

Continuing with the example, if the 20 candidate rules are present onall five tanks within the plant when producing undesired status data,predictive maintenance program 300 utilizes a one-sample chi-squaredtest to determine a uniform distribution of the 20 candidate rules basedon a temperature sensor on each tank. Predictive maintenance program 300specifies that a candidate rule must be distributed among all entitieswithin a specified degree of fit. Predictive maintenance program 300determines that 6 of the candidate rules are not uniformly distributedamong the five entities within the specific degree of fit and 14 of thecandidate rules are distributed among the five entities within thespecific degree of fit. The six candidate rules are considered as notbeing uniformly distributed within undesired status data.

Processing proceeds to operation S260, where candidate rule exclusionmod 360 excludes candidate rules not being uniformly distributed withinundesired status data. Any candidate rule not uniformly distributedaccording to operation S258 will not be considered as an influentialrule that describes undesired status data. In this embodiment, the 6candidate rules that were determined to not be uniformly distributedamong the five tanks are excluded from being considered as aninfluential rule. This leaves 14 candidate rules for further evaluationas a sub-set of the candidate rules.

Processing proceeds to operation S262, where uniform distributiondetermination mod 358 determines if the sub-set of candidate rules areuniformly distributed within desired status data. Predictive maintenanceprogram 300 evaluates if the candidate rule occurs on a specifiedpercentage of entities with desired status. The specified percentage ofentities with desired status may be set by a user or via computerdetermination. the sub-set of candidate rules are evaluated to see ifthey have a significant presence in the desired status data. Statisticalmethods may be employed to determine how significant of a presencecandidate rules have in the desired status data. For example, aone-sample chi-squared test may be employed to determine if thecandidate rule is equally distributed throughout each entity. Theminimum degree of fit may be specified for predictive maintenanceprogram 300. Predictive maintenance program 300 may specify that acandidate rule must be distributed among all entities within a specificdegree of fit.

According to the example, predictive maintenance program 300 specifiesthat a candidate rule must be present on no more than 25% of the tankswhen producing desired status data. The 14 remaining candidate rules arepresent on less than a quarter of all tanks when operating according toa desired status. Predictive maintenance program 300 utilizes aone-sample chi-squared test to determine uniform distribution across thefive tanks (the temperature sensors on the five tanks). Predictivemaintenance program 300 determines that 10 of the remaining candidaterules are not uniformly distributed among the five tanks within thespecified degree of fit and 4 of the remaining candidate rules aredistributed uniformly among the five tanks within the specified degreeof fit. Therefore, four of the candidate rules are determined to beuniformly distributed within desired status data.

Processing proceeds to operation S264, where candidate rule exclusionmod 360 excludes candidate rules that are uniformly distributed withindesired status data. Any candidate rule determined to be uniformlydistributed in operation S260 will not be considered as an influentialrule that describes undesired status data. The remaining candidate rulesfrom the sub-set of candidate rules are a final sub-set of candidaterules for evaluation to be influential rules. In this example, the fourcandidate rules that were determined to be uniformly distributed amongthe five tanks when producing desired status data are not included inthe final sub-set of candidate rules, leaving 10 candidate rules forfurther evaluation.

Processing proceeds to operation S266, where candidate rule significancedetermination mod 366 determines if candidate rules are significant withrespect to undesired status data. The final sub-set of candidate rulesare evaluated by comparing the presence of a candidate rule in bothdesired and undesired status data. Statistical methods support adetermination of how significant the presence of the candidate rule isfor undesired status data.

Continuing with the example, a one-sample chi-squared test is employedto determine if the rule is equally distributed throughout each entity.For another example, a minimum degree of fit is specified for predictivemaintenance program 300. Rule distributions for an influential ruleshould not be similar for both desired and undesired status data. Inthis embodiment, predictive maintenance program 300 utilizes aone-sample chi-squared test to determine significance of rules based atleast on the data collected by the temperature sensors on the fivetanks. Predictive maintenance program 300 determines that two candidaterules of the final sub-set of 10 candidate rules are similar betweendesired and undesired status data within the specific degree of fit and8 of the candidate rules are not similar among the five tanks within thespecific degree of fit. The 2 candidate rules are determined to be notsignificant between desired and undesired status data. It should benoted that if the number of times one rule occurs for one entityproducing desired status data is more than the sum of the number ofrules occurring in all other entities, the process may still find thatthe one rule will cause an undesired result, that rule should not beconsidered as an influential rule.

Processing proceeds to operation S268, where candidate rule exclusionmod 360 excludes candidate rules not significant between desired statusdata and undesired status data. Any candidate rule determined to be notsignificant in operation S266 will not be considered as an influentialrule that describes undesired status data. In this embodiment, the 2candidate rules, that were determined to be not significant among thefive tanks with desired status data and undesired status data, areexcluded from being an influential rule, leaving 8 candidate rulesidentified as influential rules.

Processing proceeds to operation S270, where influential rulesgeneration mod 370 generates influential rules and correspondinginfluential rates to establish a predictive maintenance process thatmonitors for the influential rules. The candidate rules not excluded bythe candidate rule exclusion module are deemed influential rules.Influential rules those rules determined to be useful for predictivemaintenance operations. Each influential rule has a correspondinginfluential rate. The influential rate is calculated using the followinginfluential rate formula:

${{{Influential}\mspace{14mu} {rate}} = {\frac{B_{e}}{B_{t}}\text{/}( {\frac{B_{e}}{B_{t}} + \frac{G_{e}}{G_{t}}} )}},$

where: B_(e) is the number of entities with undesired status and therule present, B_(t) is the total number of entities with undesiredstatus, G_(e) is the number of entities with desired status and the rulepresent, and G_(t) is the total number of entities with desired status.In this example embodiment, the 8 remaining candidate rules aregenerated as influential rules and are assigned a correspondinginfluential rate. The influential rate of each rule is calculated by theinfluential rules generation module according to the influential rateformula.

In some embodiments of the present invention, influential rulesgenerated by influential rules generation mod 370 are supplied to arules engine for automated predictive maintenance operations.Influential rules that result from the above-described process describesequences of events that are generally applicable to a process forpredicting a failure or an undesired result, also referred to herein asan error event. Continuing with the example, the eight influential rulesrepresent generally applicable sequences of events that lead to anundesired temperature of a chemical in a tank. Accordingly, predictivemaintenance program 300 monitors chemical processes in the five tanksfor an occurrence of a sequence of events that match one of the eightinfluential rules. Upon detection of an influential rule being met, thetemperature of the chemical in the tank is adjusted or, in some cases,monitored more closely than usual because the temperature is likely tobecome undesired. The final set of influential rules for use may be lessthan all the candidate rules deemed influential by the influential rulesgeneration module. Such variation will be due to a pre-defined limit onthe number of influential rules reported, or due to other considerationsthat have the effect of reduced total count of influential rulesreported.

III. Further Comments and/or Embodiments

Some embodiments of the present invention recognize the following facts,potential problems, and/or potential areas for improvement with respectto the current state of the art. Sequence rule mining used forpredictive maintenance generally assumes events occur sequentially.While many events tend to occur sequentially, sequential rule miningcannot handle the situation when a critical event occurs at one time anddoes not at others even when there is a similar sequential order in bothcases. For example, an entity can report events A, B, C occurredsequentially and produced a “not normal” status, referred to above as“undesired” status. However, another entity reports events A, B, Coccurred sequentially and produced a “normal” status, referred to aboveas “desired” status. In this example, sequential rule mining would havea difficult time predicting “not normal” or “normal” status for anentity that reports A, B, C occurring sequentially.

Some embodiments of the present invention may include one, or more, ofthe following features, characteristics, and/or advantages: (i) asupervised sequence rule mining method to recognize statisticallysignificant patterns to recognize “not normal” or “normal” status; (ii)not simply pattern identification, but statistical analysis of criticalevents for pattern prediction; (iii) the timing between a critical eventand a “not normal” status does not have to be considered for predictivemaintenance; (iv) event patterns in “not normal” status data areidentified; (v) the distribution of those event patterns in “not normal”status data produced by target entities drives determination ofcandidate patterns, or rules; (vi) influential rules are determined byanalyzing “normal” status data for similar event patterns.

IV. Definitions

“Present invention” does not create an absolute indication and/orimplication that the described subject matter is covered by the initialset of claims, as filed, by any as-amended set of claims drafted duringprosecution, and/or by the final set of claims allowed through patentprosecution and included in the issued patent. The term “presentinvention” is used to assist in indicating a portion or multipleportions of the disclosure that might possibly include an advancement ormultiple advancements over the state of the art. This understanding ofthe term “present invention” and the indications and/or implicationsthereof are tentative and provisional and are subject to change duringthe course of patent prosecution as relevant information is developedand as the claims may be amended.

“Embodiment,” see the definition for “present invention.”

“And/or” is the inclusive disjunction, also known as the logicaldisjunction and commonly known as the “inclusive or.” For example, thephrase “A, B, and/or C,” means that at least one of A or B or C is true;and “A, B, and/or C” is only false if each of A and B and C is false.

A “set of” items means there exists one or more items; there must existat least one item, but there can also be two, three, or more items. A“subset of” items means there exists one or more items within a groupingof items that contain a common characteristic.

A “plurality of” items means there exists at more than one item; theremust exist at least two items, but there can also be three, four, ormore items.

“Includes” and any variants (e.g., including, include, etc.) means,unless explicitly noted otherwise, “includes, but is not necessarilylimited to.”

A “user” or a “subscriber” includes, but is not necessarily limited to:(i) a single individual human; (ii) an artificial intelligence entitywith sufficient intelligence to act in the place of a single individualhuman or more than one human; (iii) a business entity for which actionsare being taken by a single individual human or more than one human;and/or (iv) a combination of any one or more related “users” or“subscribers” acting as a single “user” or “subscriber.”

The terms “receive,” “provide,” “send,” “input,” “output,” and “report”should not be taken to indicate or imply, unless otherwise explicitlyspecified: (i) any particular degree of directness with respect to therelationship between an object and a subject; and/or (ii) a presence orabsence of a set of intermediate components, intermediate actions,and/or things interposed between an object and a subject.

A “module” is any set of hardware, firmware, and/or software thatoperatively works to do a function, without regard to whether the moduleis: (i) in a single local proximity; (ii) distributed over a wide area;(iii) in a single proximity within a larger piece of software code; (iv)located within a single piece of software code; (v) located in a singlestorage device, memory, or medium; (vi) mechanically connected; (vii)electrically connected; and/or (viii) connected in data communication. A“sub-module” is a “module” within a “module.”

A “computer” is any device with significant data processing and/ormachine readable instruction reading capabilities including, but notnecessarily limited to: desktop computers; mainframe computers; laptopcomputers; field-programmable gate array (FPGA) based devices; smartphones; personal digital assistants (PDAs); body-mounted or insertedcomputers; embedded device style computers; and/or application-specificintegrated circuit (ASIC) based devices.

The phrase “without substantial human intervention” means a process thatoccurs automatically (often by operation of machine logic, such assoftware) with little or no human input. Some examples that involve “nosubstantial human intervention” include: (i) a computer is performingcomplex processing and a human switches the computer to an alternativepower supply due to an outage of grid power so that processing continuesuninterrupted; (ii) a computer is about to perform resource intensiveprocessing and a human confirms that the resource-intensive processingshould indeed be undertaken (in this case, the process of confirmation,considered in isolation, is with substantial human intervention, but theresource intensive processing does not include any substantial humanintervention, notwithstanding the simple yes-no style confirmationrequired to be made by a human); and (iii) using machine logic, acomputer has made a weighty decision (for example, a decision to groundall airplanes in anticipation of bad weather), but, before implementingthe weighty decision the computer must obtain simple yes-no styleconfirmation from a human source.

“Automatically” means “without any human intervention.”

What is claimed is:
 1. A computer-implemented method for establishing apredictive maintenance process comprising: determining a target variablefor a process to be performed by a plurality of entities; collectingevent data from the plurality of entities while performing the process,the event data including data time stamps; storing, in a database, theevent data; recording to the database a set of values of the targetvariable during the process, the values including value time stamps;assigning to each entity a set of entity data from the collected eventdata, each set of entity data corresponding to the entity from which theevent data was collected; identifying desired periods of time when theset of values meet a desired value range of the target variable andundesired periods of time when the set of values do not meet the desiredvalue range based on the value time stamps; associating the desiredperiods of time with collected event data having corresponding data timestamps, the associated event data being produced during periods of timewhen the value of the target variable is desired; storing the associatedevent data produced during periods of time when the value of the targetvariable is desired in the database as desired result data; associatingthe undesired periods of time with collected event data havingcorresponding data time stamps, the associated event data being producedduring periods of time when the value of the target variable isundesired; storing the associated event data produced during periods oftime when the value of the target variable is undesired in the databaseas undesired result data; mining for a set of sequence rules within theundesired result data for each entity, the sequence rules being based ona chronological order of data time stamps; identifying a subset ofsequence rules having a uniform occurrence among the plurality ofentities; selecting a set of candidate sequence rules from the subset ofsequence rules based on a non-uniform distribution of the set ofcandidate sequence rules within the desired result data for each entity;and recording a set of significant sequence rules from the set ofcandidate sequence rules as a set of predictive maintenance rules for apredictive maintenance process based on a disparate distribution of theset of significant sequence rules when comparing distributions of theoccurrence of candidate sequence rules in desired result data andundesired result data.
 2. The computer-implemented method of claim 1,wherein the entities are manufacturing devices.
 3. Thecomputer-implemented method of claim 1, wherein: the target variable isa process temperature; and the event data is at least collected from atemperature sensor.
 4. The computer-implemented method of claim 1,wherein mining for a set of sequence rules includes mining onlythree-event sequence rules.
 5. The computer-implemented method of claim1, wherein identifying the subset of sequence rules includes applying aone-sample-chi-square test to the set of sequence rules.
 6. Thecomputer-implemented method of claim 1, wherein selecting a set ofcandidate sequence rules includes determining a frequency of occurrenceof the subset of sequence rules within the desired result data.
 7. Acomputer program product for establishing a predictive maintenanceprocess comprising a computer readable storage medium having storedthereon: first program instructions programmed to determine a targetvariable for a process to be performed by a plurality of entities;second program instructions programmed to collect event data from theplurality of entities while performing the process, the event dataincluding data time stamps; third program instructions programmed tostore, in a database, the event data; fourth program instructionsprogrammed to record to the database a set of values of the targetvariable during the process, the values including value time stamps;fifth program instructions programmed to assign to each entity a set ofentity data from the collected event data, each set of entity datacorresponding to the entity from which the event data was collected;sixth program instructions programmed to identify desired periods oftime when the set of values meet a desired value range of the targetvariable and undesired periods of time when the set of values do notmeet the desired value range based on the value time stamps; seventhprogram instructions programmed to associate the desired periods of timewith collected event data having corresponding data time stamps, theassociated event data being produced during periods of time when thevalue of the target variable is desired; eighth program instructionsprogrammed to store the associated event data produced during periods oftime when the value of the target variable is desired in the database asdesired result data; ninth program instructions programmed to associatethe undesired periods of time with collected event data havingcorresponding data time stamps, the associated event data being producedduring periods of time when the value of the target variable isundesired; tenth program instructions programmed to store the associatedevent data produced during periods of time when the value of the targetvariable is undesired in the database as undesired result data; eleventhprogram instructions programmed to mine for a set of sequence ruleswithin the undesired result data for each entity, the sequence rulesbeing based on a chronological order of data time stamps; twelfthprogram instructions programmed to identify a subset of sequence ruleshaving a uniform occurrence among the plurality of entities; thirteenthprogram instructions programmed to select a set of candidate sequencerules from the subset of sequence rules based on a non-uniformdistribution of the set of candidate sequence rules within the desiredresult data for each entity; and fourteenth program instructionsprogrammed to record a set of significant sequence rules from the set ofcandidate sequence rules as a set of predictive maintenance rules for apredictive maintenance process based on a disparate distribution of theset of significant sequence rules when comparing distributions of theoccurrence of candidate sequence rules in desired result data andundesired result data.
 8. The computer program product of claim 7,wherein the entities are manufacturing devices.
 9. The computer programproduct of claim 7, wherein: the target variable is a processtemperature; and the event data is at least collected from a temperaturesensor.
 10. The computer program product of claim 7, wherein mining fora set of sequence rules includes mining only three-event sequence rules.11. The computer program product of claim 7, wherein identifying thesubset of sequence rules includes applying a one-sample-chi-square testto the set of sequence rules.
 12. The computer program product of claim7, wherein selecting a set of candidate sequence rules includesdetermining a frequency of occurrence of the subset of sequence ruleswithin the desired result data.
 13. A computer system for establishing apredictive maintenance process comprising: a processor set; and acomputer readable storage medium; wherein: the processor set isstructured, located, connected, and/or programmed to run programinstruction stored on the computer readable storage medium; and theprogram instructions include: first program instructions programmed todetermine a target variable for a process to be performed by a pluralityof entities; second program instructions programmed to collect eventdata from the plurality of entities while performing the process, theevent data including data time stamps; third program instructionsprogrammed to store, in a database, the event data; fourth programinstructions programmed to record to the database a set of values of thetarget variable during the process, the values including value timestamps; fifth program instructions programmed to assign to each entity aset of entity data from the collected event data, each set of entitydata corresponding to the entity from which the event data wascollected; sixth program instructions programmed to identify desiredperiods of time when the set of values meet a desired value range of thetarget variable and undesired periods of time when the set of values donot meet the desired value range based on the value time stamps; seventhprogram instructions programmed to associate the desired periods of timewith collected event data having corresponding data time stamps, theassociated event data being produced during periods of time when thevalue of the target variable is desired; eighth program instructionsprogrammed to store the associated event data produced during periods oftime when the value of the target variable is desired in the database asdesired result data; ninth program instructions programmed to associatethe undesired periods of time with collected event data havingcorresponding data time stamps, the associated event data being producedduring periods of time when the value of the target variable isundesired; tenth program instructions programmed to store the associatedevent data produced during periods of time when the value of the targetvariable is undesired in the database as undesired result data; eleventhprogram instructions programmed to mine for a set of sequence ruleswithin the undesired result data for each entity, the sequence rulesbeing based on a chronological order of data time stamps; twelfthprogram instructions programmed to identify a subset of sequence ruleshaving a uniform occurrence among the plurality of entities; thirteenthprogram instructions programmed to select a set of candidate sequencerules from the subset of sequence rules based on a non-uniformdistribution of the set of candidate sequence rules within the desiredresult data for each entity; and fourteenth program instructionsprogrammed to record a set of significant sequence rules from the set ofcandidate sequence rules as a set of predictive maintenance rules for apredictive maintenance process based on a disparate distribution of theset of significant sequence rules when comparing distributions of theoccurrence of candidate sequence rules in desired result data andundesired result data.
 14. The computer system of claim 13, wherein theentities are manufacturing devices.
 15. The computer system of claim 13,wherein: the target variable is a process temperature; and the eventdata is at least collected from a temperature sensor.
 16. The computersystem of claim 13, wherein mining for a set of sequence rules includesmining only three-event sequence rules.
 17. The computer system of claim13, wherein identifying the subset of sequence rules includes applying aone-sample-chi-square test to the set of sequence rules.
 18. Thecomputer system of claim 13, wherein selecting a set of candidatesequence rules includes determining a frequency of occurrence of thesubset of sequence rules within the desired result data.